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Record W7032813411

An Oblique Blackness: Reading Racial Formation in the Aesthetics of George Elliott Clarke, Dionne Brand, and Wayde Compton

2013· dissertation· en· W7032813411 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2013
Typedissertation
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
Fundersnot available
KeywordsPoeticsCitizenshipNarrativeEthnic groupRacismEpithetGeorge (robot)Relation (database)Reading (process)
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines how the poetics of George Elliott Clarke, Dionne Brand and Wayde Compton articulate unique aesthetic voices that are representative of a range of ethnic communities that collectively make-up blackness in Canada. Despite the different backgrounds, geographies, and ethnicities of these authors, blackness in Canada is regularly viewed as a homogeneous community that is most closely tied to the cultural histories of the American South and the Atlantic slave trade. Black Canadians have historically been excluded from the official narratives of the nation, disassociating blackness from Canadian-ness. Epithets such as “African-Canadian” are indicative of the way race distances citizenship and belonging. Each of these authors expresses an aesthetic through their poetics that is representative of the unique combination of social, political, cultural, and ethnic interactions that can be collectively described as racial formation. While each of these authors orients her or his own ethnic community in relation to the nation in different ways, their focus on collapsing the distance between citizenship and belonging can be read as a base for forming community from which collective resistance to the racial violence of exclusion can be grounded.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.223
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.027
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.262
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

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